Curriculum & Core Subjects

The CSB Ph.D. curriculum has two components: the core subjects and advanced electives. Core subjects provide foundational knowledge of both biology and computational biology. Advanced electives are chosen by each student to generate a customized program of study, in close consultation with members of the CSB Ph.D. Graduate Committee and the student's thesis advisor. The goal is to allow students broad latitude in defining their individual area of interest, but at the same time to provide oversight and guidance to ensure that they receive rigorous and thorough training.

Core Subjects

The core curriculum consists of three classroom subjects plus a set of three two-month rotations in different research groups. The classroom subjects fall into three areas:

Topics in Computational and Systems Biology (One Subject): All first-year students in the program are required to participate in this literature-based exploration of current research frontiers and paradigms. Papers for discussion are selected from a broad range of topics in computational and systems biology, with an emphasis on the integration of experimental and computational approaches to understanding complex biological systems. This subject is limited to students in the CSB Ph.D. Program in order to build a strong community among the class. It is the only subject in the program with such a limitation.

CSB.100 Topics in Computational and Systems Biology

Modern Biology (One Subject):

A semester of modern graduate-level biology at MIT strengthens the biology base of all students in the program. Subjects in molecular biology, neurobiology, biochemistry, or genetics fulfill this requirement. The particular course taken by each student will depend on his or her background and will be determined in consultation with members of the CSB Ph.D. Graduate Committee. Subjects that can fulfill the biology requirement for the CSB Ph.D. degree include: (choose one)

Computational Biology (One Subject):

1. 6.8700/HST.507 J Advanced Computational Biology: Genomes, Networks, Evolution. This course additionally examines recent publications in the areas covered, with research-style assignments. A more substantial final project is expected, which can lead to a thesis and publication,

2. 7.81/8.591 J Systems Biology This graduate-level course explores more in-depth cellular and population-level systems with an emphasis on synthetic biology, modeling of genetic networks, cell-cell interactions, and evolutionary dynamics.

3. 20.490 Computational Systems Biology: Deep Learning in the Life Sciences Presents innovative approaches to computational problems in the life sciences, focusing on deep learning-based approaches with comparisons to conventional methods. Topics include protein-DNA interaction, chromatin accessibility, regulatory variant interpretation, medical image understanding, medical record understanding, therapeutic design, and experiment design (the choice and interpretation of interventions). Focuses on machine learning model selection, robustness, and interpretation. Teams complete a multidisciplinary final research project using TensorFlow or other framework. Provides a comprehensive introduction to each life sciences problem, but relies upon students understanding probabilistic problem formulations. Students taking graduate version complete additional assignments.

4. Both a and b below:

a. 6.C51Modeling with Machine Learning: from Algorithms to Applications focuses on modeling with machine learning methods with an eye towards applications in engineering and sciences. Introduction to modern machine learning methods, from supervised to unsupervised models, with an emphasis on newer neural apporaches. Emphasis on the understanding of how and why the methods work from the point of view of modeling, and when they are applicable. Unsing concrete examples, covers formulation of machine learning tasks, adapting and extending methods to given problems, and how the methods can and should be evaluated. Students taking graduate version complete additonal assignments. Students cannot receive credit without simultaneous completion of a 6-unit disciplinary module. Enrollment may be limited.

b. 20.C51/3.C51/10.C51J Machine Learning for Molecular Engineering Building on core material in 6.C51, provides an introduction to the use of machine learning to solve problems arising int he science and engineering of biology, chemistry, and materials. Equips students to design and implement achine learning approaches to challenges such as analysis of omics (genomics, transcriptomics, proteomics, etc.) microscopy, spectroscopy, or crystallography data and design of new molecules and materials such as drugs, catalysts, polymer, alloys, ceramics, and proteins. Students taking graduat version complete addiitonal assignments. Students cannot receive credit with simultaneous completion of 6.CS1.

6.C51 & 20.C51 MUST BE TAKEN TOGETHER IN THE SAME SEMESTER